Decadal prediction of Sahel rainfall using dynamics-based indices
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At decadal time scales, the capability of state-of-the-art atmosphere-ocean coupled climate models in predicting the precipitation in Sahel is assessed. A set of 14 models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) is selected and two experiments are analysed, namely initialized decadal hindcasts and forced historical simulations. Considering the strong linkage of the atmospheric circulation signatures over West Africa with the rainfall variability, this study aims to investigate the potential of using wind fields for decadal predictions. Namely, a West African monsoon index (WAMI) is defined, based on the coherence of low (925 hPa) and high (200 hPa) troposphere wind fields, which accounts for the intensity of the monsoonal circulation. A combined empirical orthogonal functions analysis is applied to explore the wind fields’ covariance modes, and a set of indices is defined on the basis of the identified patterns. The WAMI predictive skill is assessed by comparing WAMI from coupled models with WAMI from reanalysis products and with a standardized precipitation index (SPI) from observations. Results suggest that the predictive skill is highly model dependent and it is strongly related to the WAMI definition. In addition, hindcasts are more skilful than historical simulations in both deterministic and probability forecasts, which suggests an added value of initialization for decadal predictability. Moreover, coupled models are more skilful in predicting the observed SPI than the WAMI obtained from reanalysis. WAMI performance is also compared with decadal predictions from CMIP5 models based on a Sahelian precipitation index, and an improvement in predictive skill is observed in some models when WAMI is used. Therefore, we conclude that dynamics-based indices are potentially more effective for decadal prediction of precipitation in Sahel than precipitation-based indices for those models in which Sahel rainfall variability is not well simulated. We thus recommend a two-fold approach when testing the performance of models in predicting Sahel rainfall, based not only on rainfall but also on the dynamics of the West African monsoon.
KeywordsStandardize Precipitation Index Anomaly Correlation Coefficient ERA40 Reanalysis Decadal Time Scale West African Monsoon
We thank the two anonymous reviewers for the comments and suggestions, which helped us improve the manuscript. The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 603521 and the Spanish Project CGL2012-38923-C02-01. We acknowledge the World Climate Research Program’s Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modelling groups for producing and making available their model output. For CMIP the U.S. Department of Energy’s Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals.
- Doblas-Reyes FJ, Weisheimer A, Palmer TN, Murphy JM, Smith D (2010) Forecast quality assessment of the ENSEMBLES seasonal-to-decadal stream 2 hindcasts. ECMWF Technical Memoranda 621Google Scholar
- Gaetani M, Fontaine B (2013) Interaction between the West African Monsoon and the summer Mediterranean climate: an overview. Física de la Tierra 25:41–55Google Scholar
- Ickowicz A, Ancey V, Corniaux C, Duteurtre G, Poccard-Chappuis R, Toure I, Vall E and Wane A (2012) Crop-livestock production systems in the Sahel—increasing resilience for adaptation to climate change and preserving food security. Building resilience for adaptation to climate change in the agriculture sector FAO/OECD Rome 243–276Google Scholar
- International CLIVAR Project Office (ICPO) (2011) Data and bias correction for decadal climate predictions. International CLIVAR Project Office CLIVAR Publication Series 150:6Google Scholar
- Kandji ST, Verchot S, Mackensen J (2006) Climate Change and variability in the Sahel region: impacts and adaptation strategies in the Agricultural sector. World Agroforestry Centre (ICRAF) and United Nations Environment Programme (UNEP). UNEP 2006:1–48Google Scholar
- Kirtman B, Power SB, Adedoyin JA et al (2013) Near-term climate change: projections and predictability. In: Stocker TF, Qin D, Plattner G-K, Tignor M, Allen SK, Boschung J, Nauels A, Xia Y, Bex V, Midgley PM (eds) Climate change 2013: the physical science basis. Contribution of Working Group I to the fifth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UKGoogle Scholar
- Martin ER, Thorncroft C (2014) Sahel rainfall in multimodel CMIP5 decadal hindcasts. Geophys Res Lett: 41. doi: 10.1002/2014GL059338
- McIntire J (1981) Food security in the Sahel: variable import levy, grain reserves and foreign exchange assistance. Research report 26 International Food Policy Research Institute Washington USAGoogle Scholar
- Mohino E, Rodríguez-Fonseca B, Losada T, Gervois S, Janicot S, Bader J, Ruti P, Chauvin F (2011b) SST-forced signals on West African rainfall from AGCM simulations-Part I: changes in the interannual modes and model intercomparison. Clim Dyn 37:1707–1725. doi: 10.1007/s00382-011-1093-2 CrossRefGoogle Scholar
- Trenberth KE et al (2007) Observations: Surface and atmospheric climate change. In: Climate change 2007: the physical science basis. Contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change [Solomon S et al (eds)]. Cambridge University Press, Cambridge and New York, NYGoogle Scholar
- Venegas SA (2001) Statistical methods for signal detection in climate. Danish Center for Earth System Science Rep 2:46Google Scholar